Global Radiotherapy: Current Status and Future Directions—White Paper
Bibliographic record
Abstract
Recognizing the increase in cancer incidence globally and the need for effective cancer control interventions, several organizations, professional bodies, and international institutions have proposed strategies to improve treatment options and reduce mortality along with minimizing overall incidence. Despite these efforts, an estimated 9.6 million deaths in 2018 was attributed to this noncommunicable disease, making it the second leading cause of death worldwide. Left unchecked, this will further increase in scale, with an estimated 29.5 million new cases and 16.3 million deaths occurring worldwide in 2040. Although it is known and generally accepted that cancer services must include radiotherapy, such access is still very limited in many parts of the world, especially in low- and middle-income countries. After thorough review of the current status of radiotherapy including programs worldwide, as well as achievements and challenges at the global level, the International Atomic Energy Agency convened an international group of experts representing various radiation oncology societies to take a closer look into the current status of radiotherapy and provide a road map for future directions in this field. It was concluded that the plethora of global and regional initiatives would benefit further from the existence of a central framework, including an easily accessible repository through which better coordination can be done. Supporting this framework, a practical inventory of competencies needs to be made available on a global level emphasizing the knowledge, skills, and behavior required for a safe, sustainable, and professional practice for various settings. This white paper presents the current status of global radiotherapy and future directions for the community. It forms the basis for an action plan to be developed with professional societies worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".